Deep submicron technology makes interconnect one of the main factors determining the circuit performance. Previous work shows that interconnect parameters exhibit a significant amount of spatial variation. In this work, we develop approaches to study the influence of the interconnect variation on circuit performance and to evaluate the circuit sensitivity to interconnect parameters. First, an accurate interconnect modeling technique is presented, and an interconnect model library is developed. Then, we explore an approach using parameterized interconnect models to study circuit sensitivity via a ring oscillator circuit. Finally, we present an alternative approach using statistical experimental design techniques to study the sensitivity of a large and complicated circuit to interconnect variations. Index Terms---Circuit analysis, interconnect, statistical analysis, worst case design. I. INTRODUCTION T HE CONTINUOUSLY increasing scale of integration used in the design and processing ...

A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others have finished. One critical performance measure for an activity network is its makespan, which is the minimum time required to complete all activities. In a stochastic activity network (SAN), the durations of the activities and the makespan are random variables. The analysis of SANs is quite involved, but can be carried out numerically by Monte Carlo analysis. This paper concerns the optimization of a SAN, i.e., the choice of some design variables that affect the probability distributions of the activity durations. We concentrate on the problem of minimizing a quantile (e.g., 95%) of the makespan, subject to constraints on the variables. This problem has many applications, ranging from project management to digital integrated circuit (IC) sizing (the latter being our motivation). While there are effective methods for optimizing DANs, the SAN optimization problem is much more difficult; the few existing methods cannot handle large-scale problems.

Process variation has forever been the major fail cause of analog circuit where small deviations in component values cause large deviations in the measured output parameters. This paper presents a new approach for parametric fault simulation and test vector generation. The proposed approach utilizes the process information and the sensitivity of the circuit principal components in order to generate statistical models of the fault-free and the faulty circuit. The obtained information is then used as a measurement to quantify the testability of the circuit. This approach extended by hard fault testing has been implemented as automated tool set for IC testing called FaultMaxx and TestMaxx.

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This paper presents a simulator for the statistical analysis of MOS integrated circuits affected by mismatch effect. The tool is based on a rigorous formulation of circuit equations including random current sources to take into account technological tolerances. The simulator requires a simulation time of several orders of magnitude lower than that required by Montecarlo analysis, while ensuring a good accuracy.

Towards predictable deep-submicron manufacturing

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